Abstract
The activities of the electricity sector in production and consumption have implications in almost all environmental problems of today. The main environmental impacts occur during the production of electricity, mainly due to the emission of air pollutants, which is directly linked to climate change that has been observed over time. Ambitious climate change mitigation requires significant changes in many economic sectors, in particular in the production and consumption of energy. Considering that the primary energy consumption has increased, doubling since the 1970s, and in particular the consumption of electricity has had a sharper increase, nearly quadrupling in the same period, all measures that can mitigate environmental impacts on both the supply and demand sides of electricity are of interest. This paper introduces a new software application that analyses efficient investment in street lighting and industrial and domestic electrical equipment, accounting for the reduction of losses in the conductors of electrical installations, which is usually neglected. It also determines the reduction of CO2 emitted into the atmosphere, which contributes to the reduction of emissions of greenhouse gases from a country or particular product.

Abstract
Mobility and commuting in metropolitan areas are very expensive, highly polluted and time wasting. The Four Step Model (FSM) is the key model to analyze a Transportation Network. However, being the FSM a combination of several models, combining them in one model have rarely been applied. To deal with this problem an Agent-Based Model (ABM) is proposed. An ABM uses the metaphor of autonomous agents and so, they can be a handful tool for combining different models in one. Therefore, this model can be used as a tool for simulation and integrate the FSM in one model. Here we present the preliminary results of this approach. (C) 2014 The Authors. Published by Elsevier B.V.

Abstract
Nowadays information is generated and gathered from distributed streaming data sources, stressing communications and computing infrastructure, making it hard to transmit, compute, and store. Knowledge discovery from ubiquitous data streams has become a major goal for all sorts of applications, mostly based on unsupervised techniques such as clustering. Two subproblems exist: clustering streaming data observations and clustering streaming data sources. The former searches for dense regions of the data space, identifying hot spots where data sources tend to produce data, while the latter finds groups of sources that behave similarly over time. In order to better assess the current status of this topic, this article presents a thorough review on distributed algorithms addressing either of the subproblems. We characterize clustering algorithms for ubiquitous data streams, discussing advantages and disadvantages of distributed procedures. Overall, distributed stream clustering methods improve communication ratios, processing speed, and resources consumption, while achieving similar clustering validity as the centralized counterparts. (C) 2013 John Wiley & Sons, Ltd.

Abstract
The value of research data is recognized, and so is the importance of the associated metadata to contextualize, describe and ultimately render them understandable in the long term. Laboratory notebooks are an excellent source of domain-specific metadata, but this paper-based approach can pose risks of data loss, while limiting the possibilities of collaborative metadata production. The paper discusses the advantages of tools to complement paper-based laboratory notebooks in capturing metadata, regardless of the research domain. We propose LabTablet, an electronic laboratory book aimed at the collection of metadata from the early stages of the research workflow. To evaluate the use of LabTablet and the proposed workflow, researchers in two domains were asked to perform a set of tasks and provided insights about their experience. By rethinking the workflow and helping researchers to actively contribute to data description, the research outputs can be described with generic and domain-dependent metadata, thus improving their chances of being deposited, reused and preserved.

Abstract

Abstract
Personality traits play a key role in the shaping of emotions, moods, cognitions, and behaviours of individuals interacting in a virtual environment. The personalities one exhibits reflect one's perception of the world and are demonstrated in the act of communication. Thus, the evaluation of a message can be changed due to stress and mood variations. Being able to identify the degree of relationship between one's personality characteristics and one's current stress state can thus facilitate the communication process. In particular, in this paper it is studied the correlation between some personality traits and the stress levels exhibited by users' interactions. To do so a novel approach was followed in which an intelligent environment is used to support the stress recognition process providing important personality related information. An experiment has been designed for the purpose of addressing the estimation of relevant aspects of interactions that occur in a rich sensory environment. Outputs from the experiment, such as the relation between personality characteristics and stress, can be used to maximize the benefits of virtual environments and its applications in fields such as learning, medicine or conflict resolution. © 2014 Springer International Publishing Switzerland.